https://popups.uliege.be/esaform21/index.php?id=1691 Index terms fr 0 https://popups.uliege.be/esaform21/index.php?id=4298 In many branches of production, components using large number of joints are combined together to make complex structures. The use of mechanical joining techniques offers the possibility to join structures with a wide range of material/geometry configurations. Due to changing in material properties during the production of formed parts, the robustness of the joint must be guaranteed. In this regard, a numerical method has been developed to predict the geometrical properties of the joint as a function of pre-straining of the metal sheets. In this way, the material combination and the joining tools are to be considered. The resulting metamodels were used to estimate the robustness of the joining process. In this study, the method is extended by a numerical load capacity model, which is generated from the joining process model using an automatic algorithm. The simulation model used for predicting the load capacity is validated by experiments. It is shown that the resulting automatic method is able to completely map a process chain and to predict the load capacity of the mechanical joints under consideration of the pre-strain. Furthermore, the correlation between the pre-strain and the load capacity is presented. Thu, 01 Apr 2021 17:54:38 +0200 Mon, 12 Apr 2021 11:38:39 +0200 https://popups.uliege.be/esaform21/index.php?id=4298 https://popups.uliege.be/esaform21/index.php?id=4277 Driven by the CO2-emission law by the European government and the increasing costs for raw materials as well as energy, the automotive industry is increasingly using multi-material constructions. This leads to a continuous increase in the use of mechanical joining techniques and especially the self-piercing riveting is of particular importance. The reason for this is the wide range of joining possibilities as well as the high load-bearing capacities of the joints. To be able to react to changing boundary conditions, like material thickness or strength variation of the sheets, research work is crucial with regard to the increase of versatility. In this paper, a numerical study of the influences on the selfpiercing riveting process is presented. For this purpose, the influence of different process parameters such as rivet length and die depth on various quality-relevant characteristics were investigated. With the help of the design of experiment, significant influences were determined and interactions between the individual parameters are shown. Thu, 01 Apr 2021 17:46:14 +0200 Mon, 12 Apr 2021 11:37:54 +0200 https://popups.uliege.be/esaform21/index.php?id=4277 https://popups.uliege.be/esaform21/index.php?id=3649 The determination of ideal process parameters for mechanical joining processes such as self-pierce riveting currently requires a comprehensive understanding of the process, the availability of the materials to be joined and the corresponding system technology. General process models can simplify the use of these joining technologies, accelerate development cycles and thereby reduce the effort for implementation into production. In this paper, the development of general data-based process models for the mechanical joining method self-pierce riveting with semi-tubular rivet is described. Extensive experimental and numerical investigations with more than 2300 joint combinations for steel and aluminum sheets with tensile strengths between 240 - 1020 MPa were generated for the building of the models. Based on these results, different meta-models are fused into general data-based process models for the self-pierce riveting process in order to show the general relationships between material properties, process parameters and joining results. The paper discusses the acquisition of the experimental and numerical data, the statistical methods for evaluation and the application of the data-based process models. Mon, 29 Mar 2021 13:55:44 +0200 Fri, 09 Apr 2021 10:35:25 +0200 https://popups.uliege.be/esaform21/index.php?id=3649 https://popups.uliege.be/esaform21/index.php?id=4302 The phenomenon of tool wear strongly affects the efficiency of machining and the quality of machined products. The experimental approach to investigate tool wear requires several time consuming tests. Finite Element Methods (FEM) can be utilized to predict tool wear and tool life as function of process parameters and tool geometry. The commercial software for Finite Element Analysis (FEA) are limited by the impossibility to update the geometry of the worn tool. This research utilizes a self-released subroutine in order to modify the tool geometry in DEFORM 3D simulations by considering the volume reduction of the tool. The model was validated with experimental data obtained by drilling tests on Inconel 718 using conventional metal working fluids (MWF). The correct profile of the simulated worn tool was individuated by comparing the prediction of the simulation with the real tool geometry. The FEM simulation allowed to predict how torque changes during the tool life. In a predictive maintenance perspective, the model can be implemented to optimize the tools replacement. Thu, 01 Apr 2021 17:57:27 +0200 Thu, 01 Apr 2021 17:57:27 +0200 https://popups.uliege.be/esaform21/index.php?id=4302